Drive innovation in AI-powered medical imaging solutions, focusing on early cancer detection and improved patient outcomes.
Requirements
- Expertise in deep learning frameworks such as TensorFlow, Keras, or PyTorch.
- Advanced programming skills in Python and C++.
- Experience with large-scale data processing and complex medical datasets (e.g., DICOM, mammography, CT, MRI).
- Strong understanding of AI model validation, regulatory requirements, and healthcare environments.
- Proven publication history in top-tier AI and medical imaging journals or conferences.
- Ability to generate patentable ideas and contribute to intellectual property development.
- Preferred: Experience with explainable AI (XAI), cloud deployment (AWS, GCP), biostatistics, and clinical workflows.
Responsibilities
- Design, develop, and optimize AI and machine learning models, particularly deep learning algorithms, for medical imaging applications.
- Conduct research on innovative AI methodologies and techniques relevant to healthcare and medical image analysis.
- Preprocess, annotate, and analyze large-scale medical datasets to improve AI model performance and accuracy.
- Integrate AI solutions into production environments and validate model performance in real-world clinical settings.
- Lead testing, validation, and documentation of AI models to meet regulatory standards, including FDA compliance.
- Mentor junior researchers and contribute to cross-functional R&D initiatives.
- Support research publications, white papers, and technical documentation to communicate the impact and benefits of AI innovations.
Other
- Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 5+ years of R&D experience in medical imaging, computer vision, or AI, with a proven record of developing impactful AI solutions.
- Collaborate with product management to identify opportunities where AI can enhance functionality and drive market differentiation.
- Excellent analytical, communication, and collaboration skills for working with technical and non-technical stakeholders.
- Remote work flexibility.
